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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences Jurnal Presipitasi : Media Komunikasi dan Pengembangan Teknik Lingkungan Jurnal Kesehatan Lingkungan indonesia Media Statistika JURNAL SISTEM INFORMASI BISNIS Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang Jurnal Sains dan Teknologi Jurnal Simetris TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika JUITA : Jurnal Informatika WARTA Register: Jurnal Ilmiah Teknologi Sistem Informasi Journal of Information System E-Dimas: Jurnal Pengabdian kepada Masyarakat Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Jurnal Informatika INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) Jurnal Sisfokom (Sistem Informasi dan Komputer) ILKOM Jurnal Ilmiah KOMPUTIKA - Jurnal Sistem Komputer JTP - Jurnal Teknologi Pendidikan Indonesian Journal of Community Services Journal of Applied Data Sciences Jurnal Riset Teknologi Pencegahan Pencemaran Industri Indonesian Journal of Librarianship Proceeding Biology Education Conference Media Pustakawan STATISTIKA Journal of Bioresources and Environmental Sciences Scientific Journal of Informatics
Budi Warsito
Departemen Statistika, Fakultas Sains Dan Matematika, Universitas Diponegoro
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Physics Public Health Social Sciences Other

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Implementasi Metode SEMPLS, CSI, dan CLI untuk Pengukuran Loyalitas Pelanggan Shopee Menggunakan R Shiny Oktavia, Cintika; Warsito, Budi; Kadarrisman, Vincensius Gunawan Slamet
JST (Jurnal Sains dan Teknologi) Vol. 12 No. 3 (2023): Oktober
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jstundiksha.v12i3.68362

Abstract

Teknologi informasi digunakan di industri perdagangan online, Indonesia memiliki shopee sebagai industri perdagangan online. Shopee marketplace terbesar dan paling populer di Indonesia, menjadi pilihan utama bagi banyak konsumen, dan mendapatkan tingkat kepuasan pelanggan yang tinggi. Tingkat kepuasan yang tinggi belum menjamin Shopee memiliki loyalitas pelanggan hal ini dilihat dari banyaknya ulasan pelanggan yang masih membandingkan Shopee dengan marketplace lain. Pengujian yang dilakukan untuk membuktikan apakah tingkat kepuasan yang tinggi dapat membentuk loyalitas pelanggan pada shopee, dengan analisis yang melibatkan pengaruh kualitas layanan elektronik dan segel keamanan elektronik. Aplikasi dibagun menggunakan pakcet R shiny dari R Studio dengan metode structural equation model partial least square, customer satisfaction index dan customer loyalty index. Penelitian menggunakan pendekatan kuantitatif yaitu data yang digunakan berupa data primer yang diperoleh langsung dari kuesioner. Data penelitian diperoleh melalui penyebaran kuesioner yang diberikan kepada 200 orang pelanggan yang sudah pernah melakukan pembelian di Shopee yang disebarkan melalui Telegram. Hasil penelitian menunjukkan kualitas layanan elektronik dan segel keamanan elektronik berpengaruh positif dan signifikan terhadap kepuasan pelanggan. Hasil pengukuran tingkat kepuasan pelanggan menunjukkan tingkat pada kategori “Sangat Puas” dan  hasil pengukuran tingkat loyalitas pelanggan yang menunjukkan tingkat pada kategori “Sangat Loyal”. Hasil evaluasi yang telah dilakukan mengindikasikan bahwa Shopee telah berhasil memenuhi dengan baik kebutuhan pelanggan, yang dicerminkan hasil pengukuran tingkat loyalitas yang tinggi di antara pelanggan dengan Shopee.
Topic Modelling Latent Dirichlet Allocation untuk Klasifikasi Komentar pada Layanan Streaming Platform Royani, Noorhanida; Widodo, Catur Edi; Warsito, Budi
JST (Jurnal Sains dan Teknologi) Vol. 12 No. 3 (2023): Oktober
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jstundiksha.v12i3.68492

Abstract

Seiring dengan berkembangnya teknologi, memunculkan banyak platform online untuk streaming film. Streaming platform banyak digunakan masyarakat seperti netflix, disney+, hbo go, we tv, vidio. Banyaknya perbandingan antar streaming platform menjadi perbincangan dimedia sosial yaitu twitter. Opini yang disampaikan pengguna streaming platform berisi komentar positif dan komentar negatif yang mempengaruhi pengguna lainnya yang ingin menonton film. Penelitian ini dilakukan untuk mengkaji perbandingan antara komentar positif dan komentar negatif pengguna streaming platform pada media sosial Twitter. Metode Latent dirichlet allocation dapat digunakan sebagai topic modelling dan Support Vector Machine untuk klasifikasi. Pada tahapan pengambilan data dengan menggunakan tools framework scrapy dengan python, data diambil sebanyak 5.000 dan dilakukan preprocessing text. Metode LDA dapat mempresentasikan topik dan dokumen serta klasifikasi menggunakan Support Vector Machine (SVM) mendapatkan hasil komentar positif lebih banyak dari pada komentar negatif. Hasil evaluasi preforma didapatkan nilai akurasi 0,88, recall 0,88, F1score 0,87, precision 0,88. Topic Modelling Latent Dirichlet Allocation (LDA) untuk Klasifikasi Komentar pada Layanan Streaming Platform dengan menggunakan 5,000 data diambil dari sosial media yaitu twitter yang terbagi menjadi komentar positif dan komentar negatif. Hasil ini dipengaruhi dari jumlah komentar positif yang lebih dominan dari pada komentar negatif. Implikasi dari penelitian ini adalah pentingnya memperhatikan keseimbangan data dalam melakukan klasifikasi komentar pada platform streaming agar hasil prediksi klasifikasi dapat lebih akurat.
Perbandingan Kinerja Inception- Resnetv2, Xception, Inception-v3, dan Resnet50 pada Gambar Bentuk Wajah Masruroh, Fitriana; Surarso, Bayu; Warsito, Budi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 1: Februari 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023104941

Abstract

Saat ini, klasifikasi bentuk wajah banyak diterapkan dalam berbagai bidang. Dalam bidang industri fashion dapat digunakan untuk pemilihan gaya rambut, pemilihan bingkai kacamata, tata rias, dan mode lainnya. Selain itu, dalam bidang medis bentuk wajah digunakan untuk bedah plastik. Identifikasi bentuk wajah adalah tugas yang menantang karena kompleksitas wajah, ukuran, pencahayaan, usia dan ekspresi. Banyak metode yang dikembangkan untuk memberikan hasil akurasi terbaik dalam klasifikasi bentuk wajah. Deep learning menjadi tren dibidang komputer vision karena memberikan hasil yang paling baik dari pada metode sebelumnya. Makalah ini mencoba menyajikan perbandingan kinerja klasifikasi wajah dengan empat arsitektur deep learning Xception, ResNet50, InceptionResNet-v2, Inception-v3. Dataset yang digunakan berjumlah 4500 gambar yang terbagi lima kelas heart, long, oblong, square, round. Berbagai pengoptimal deep learning diantaranya; transfer learning, optimizer deep learning, dropout dan fungsi aktivasi diterapkan untuk meningkatkan kinerja model. Perbandingan antara berbagai model CNN didasarkan kinerja metrik seperti accuracy, recall, precision dan F1-score. Dengan demikian dapat disimpulkan bahwa model Inception-ResNet-V2 menggunakan fungsi aktivasi Mish dan optimizer Nadam mencapai nilai tertinggi dengan accuracy dan f1-score masing-masing 92.00%, dan penggunaan waktu 65.0 menit. AbstractCurrently, face shape classification is widely applied in various fields. In the fashion industry, it can be used for hairstyle selection, eyeglass frame selection, makeup, and other modes. In the medical field, the face shape is used for plastic surgery. Identification of face shape is a challenging task due to the complexity of the face, size, lighting, age and expression. Many methods have been developed to provide the best accuracy results in the classification of face shapes. Deep learning is becoming a trend in the field of computer vision because it gives the best results than the previous method. This paper attempts to present a comparison of the performance of face classification with four deep learning architectures Xception, ResNet50, InceptionResNet-v2, Inception-v3. The dataset used is 4500 images divided into five classes heart, long, oblong, square, round. Various deep learning optimizers include; transfer learning, deep learning optimizer, dropout and activation functions are implemented to improve model performance. Comparisons between various CNN models are based on performance metrics such as accuracy, recall, precision and F1-score. Thus, it can be concluded that the Inception-ResNet-V2 model using the Mish activation function and the Nadam optimizer achieves the highest value with an accuracy and f1-score of 92.00%, and a time usage of 65.0 minutes. Thus, it can be concluded that the Inception-ResNet-V2 model using the Mish activation function and the Nadam optimizer achieves the highest value with an accuracy and f1-score of 92.00%, and a time usage of 65.0 minutes. 
Pengaruh Klasifikasi Sentimen Pada Ulasan Produk Amazon Berbasis Rekayasa Fitur dan K-Nearest Negihbor Putri, Nitami Lestari; Warsito, Budi; Surarso, Bayu
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 1: Februari 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.20241117376

Abstract

Ulasan online menjadi faktor penting yang mendorong konsumen untuk membeli barang di e-commerce. Dalam e-commerce, ulasan pelanggan sebelumnya dapat membantu pembeli membuat keputusan yang lebih baik dengan memberikan informasi tentang kualitas produk, kekuatan dan kelemahan, perilaku penjual, harga, dan waktu pengiriman. Namun, keberadaan ulasan palsu menimbulkan tantangan dalam menilai sentimen yang diungkapkan oleh pelanggan asli secara benar. Dalam penelitian ini, berfokus pada analisis sentimen dan bertujuan untuk mengeksplorasi peran sentimen dalam ulasan produk Amazon. Penelitian ini menggunakan kombinasi fitur dari konten ulasan dengan menerapkan klasifikasi K-Nearest Neighbor untuk mengklasifikasikan polaritas sentimen ulasan secara akurat. Dalam mengekstrak skor polaritas dari ulasan, penelitian ini menggunakan pendekatan analisis sentimen berbasis leksikon yaitu Textblob Library dan menetapkan label sentimen dari ulasan produk. Hasil dari pemodelan yang diusulkan mencapai tingkat akurasi sebesar 83% yang menunjukkan keefektifan pemodelan yang diusulkan dalam analisis sentimen. Hasil dari penelitian ini dapat membantu konsumen dalam membuat keputusan pembelian dan membantu penjual dalam meningkatkan nilai produk dan layanan mereka berdasarkan feedback yang diberikan oleh pelanggan.
Enhancing Bank Financial Performance Assessment: A Literature Review of Deep Learning Applications Using the Kitchenham Method Ali, Mahrus; Gernowo, Rahmat; Warsito, Budi; Muthmainah, Faliha
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 11 No 1 (2025): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v11i1.4224

Abstract

The assessment of bank financial performance is crucial for ensuring the stability of the banking sector. With advancements in technology, especially deep learning (DL), there is increasing potential to improve the accuracy of risk prediction and financial performance evaluation in banks. However, challenges related to data imbalance and model complexity require more efficient approaches. This study aims to examine the application of DL in assessing bank financial performance, with a focus on credit risk, fraud detection, and bankruptcy prediction. A Systematic Literature Review (SLR) was conducted using the Kitchenham approach, analyzing 697 relevant articles to address nine research questions regarding the implementation of DL in the banking sector. This study contributes by providing insights into effective DL models that enhance financial performance and risk prediction in banks, while also offering recommendations for the development of more transparent models. The results indicate that models such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) perform well in handling large financial data. Additionally, hybrid models that combine DL with traditional models demonstrate higher accuracy in bankruptcy prediction and fraud detection.
Development of Customer Loyalty Measurement Application Using R Shiny with Structural Equation Model Partial Least Square Method, Customer Satisfaction Index, and Customer Loyalty Index Oktavia, Cintika; Warsito, Budi; Kadarrisman, Vincensius Gunawan Slamet
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.26649

Abstract

One of Indonesia's well-known e-commerce platforms, Shopee, relies on information technology to run its business. The information technology used by Shopee is considered unable to meet customer satisfaction. Customer reviews are dissatisfied with the facilities provided by Shopee, and some customers compare Shopee with other e-commerce sites. The research contribution is the understanding that the proper use of information technology can positively impact customer experience, improve operational efficiency, and support business growth in the e-commerce industry. Research with a quantitative approach will build a website-based application as a statistical tool for data processing using R shiny so that the application results have high interactivity, dynamic visualization, and better explanation. The research will collect 100 data provided to customers who have transacted at Shopee and distributed through the telegram application, which is distributed to particular groups and channels for Shopee users. Data processing for this study will use the  Structural Equation Model Partial Least Square, Customer Satisfaction Index, Net Promoter Score, and Customer Loyalty Index. The study results show that electronic service quality and security seals positively and significantly affect customer satisfaction. Electronic service quality has a moderate effect on customer satisfaction, while electronic security seals have a slightly lower effect on customer satisfaction (t=5.584, p<0.001). Additionally, a significant correlation between customer loyalty and satisfaction was discovered (t=14.764, p=0.001). Research proves the need to improve service quality and security aspects to increase customer satisfaction on e-commerce platforms and the importance of maintaining customer satisfaction as a strategy to increase customer loyalty.
Assessing the impact of charcoal production activities on the Shea Nut tree vegetation cover Calvin, Esagu John; Warsito, Budi; Hidayat, Jafron Wasiq; Gertrude, Akello; Paul, Gudoyi M; Ahmed, Kamil
Journal of Bioresources and Environmental Sciences Vol 2, No 3 (2023): December 2023
Publisher : BIORE Scientia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jbes.2023.19260

Abstract

Charcoal remains the main energy cooking source for urban dwellers in Uganda. The Shea Nut tree produces quality charcoal which is efficient and locally made. Therefore, it is facing increasing threats from the local communities so as to meet the mushrooming demand. The study analyses the state of the Shea Nut tree, drivers of charcoal production, predict Shea Nut tree vegetation coverage, and establish mechanisms for sustainable utilization and conservation of the Shea Nut trees in Kapelebyong District. Landsat images were classified using likelihood classification in ArcGIS and interviews were conducted whilst geospatial, Stata, and Nvivo tools were used for analysis. The findings reflect a sharp declining trend in the coverage of the shea Nut trees by 2.3% and 6% from 2002-2012 and 2012-2022 respectively. The major drivers include high demand from urban areas, the need for income, and unemployment. As a result, it is predicted that by 2032, the coverage will have reduced to only 713 hectares (7.3%) from 1277 hectares (10.6%) in 2022. Therefore, charcoal production with other land uses has greatly resulted in Shea Nut tree deterioration. The study recommends the use of alternative energy sources, the provision of alternative income-generating activities for the local communities, Government of Uganda through NFA needs to enforce the ways through which Shea Nut trees are managed and utilized in order to minimize illegal cutting.
Evaluation of Waste Transportation Routes in Salatiga City Haritsa, Rifda Tsaqifarani; Maryono, Maryono; Rahadian, Rully; Hermawan, Ferry; Warsito, Budi
Jurnal Riset Teknologi Pencegahan Pencemaran Industri Vol. 16 No. 1 (2025): May
Publisher : Balai Besar Standardisasi dan Pelayanan Jasa Pencegahan Pencemaran Industri

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The problem of waste transportation is a major challenge in waste management in Salatiga City. With the amount of daily waste generated reaching 457.81 m³ and the volume transported only around 327.33 m³, the level of waste transportation has only reached 71.72%. This study aims to evaluate and optimize the current waste transportation route through a spatial approach using QGIS software. The methods used include field observation, primary and secondary data collection, and spatial analysis of the distribution of routes and workloads of the transport fleet consisting of 9 arm roll units and 1 dump truck unit, with a total average daily trip of 58 trips. The results of the comparison between the existing route and the planned route show a daily route length efficiency of 10.57 km (1.15%), fuel consumption savings of 2.73 liters per day, and travel time efficiency of 25 minutes. The volume of transported waste also increased from 83,730 kg/day to 89,500 kg/day (up 6.89%), which was achieved through more optimal route planning, additional trips to TPS Boja and Tingkir, and equalizing the workload between drivers. The results of this study confirm that GIS-based route optimization can increase the efficiency of distance, fuel, time, and productivity of the waste transportation system as a whole in Salatiga City.
UTAUT-2, HOT-Fit, and PLS-SEM for User Acceptance and Success of the Face Recognition Feature in CAT BKN Application Sari, Juwita Dwinda; Warsito, Budi; Wibowo, Catur Edi
Scientific Journal of Informatics Vol. 12 No. 4: November 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i4.31229

Abstract

Purpose: Face recognition feature was implemented in the National Civil Service Agency's Computer-Assisted Test in 2021. There has been no evaluation of the system's acceptance and success. This study aims to measure user acceptance and evaluate the feature's success using the R Shiny application. Methods: The study utilized 337 respondents from a Google Form-based questionnaire distributed throughout the Regional Office VII of the National Civil Service Agency in Palembang. The hybrid model used was UTAUT-2 and HOT-Fit, with PLS-SEM statistical analysis. Acceptance analysis and feature evaluation were conducted using the developed R Shiny Dashboard. Results: The findings indicated that 15 of the 26 hypotheses were accepted. Behavioral intention and use behavior significantly influence hedonic motivation and habit. User behavior significantly influences user satisfaction, system quality, service quality, information quality, system use, and organizational structure and environment. As users become more familiar with the technology, their experience improves, and system utilization becomes more effective. Novelty: The integration of UTAUT-2 and HOT-Fit models within an R Shiny Dashboard was applied to analyze user acceptance and evaluate the face recognition feature in Computer Computer-Assisted Test selection process. The findings provide recommendations for feature development and improving participant face recognition performance. Moreover, the R Shiny Dashboard can be adapted for user experience analysis and system evaluation in other contexts.
Unstacking the Stack: Synthesis of Optimization Strategies for Stacked Ensemble Models in Multi-Domain Contexts Widiyatmoko, Carolus Borromeus; Gernowo, Rahmat; Warsito, Budi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2545

Abstract

The implementation of stacked ensemble models (SEMs) remains widespread because they combine multiple learning algorithms into one predictive system. SEM implementations continue to struggle with accuracy limitations and overfitting problems and high computational expenses and poor interpretability issues. This review examines 269 scholarly articles from 2020 to 2025 to determine the main technical problems and their associated optimization solutions. The research presents a method engineering-based modular three-stage system which includes pre-processing, processing, and post-processing phases. The three stages address particular weaknesses by improving data quality and features, optimizing models and their parameters, and enhancing interpretability and adaptability. The framework connects SEM pipeline phases to these strategies which enable context-specific reusable design for condition-aware implementation. This research provides a systematic framework to match SEM optimization approaches with development stages which helps develop strong ensemble models that are efficient and interpretable for practical use.
Co-Authors . Widayat Abdul Hoyyi Adi Waridi Basyirudin Arifin Adi Wibowo Adi Wibowo Agus Rusgiyono Agus Winarno, Agus Ahmad Lubis Ghozali Ahmed, Kamil Alan Prahutama Anindita Nur Safira Arafa Rahman Aziz Arbella Maharani Putri Arief Rachman Hakim Arief Rachman Hakim Arief Rachman Hakim Aris Sugiharto Arsyil Hendra Saputra Atmaja, Dinul Darma Atur Ekharisma Dewi Aurum Anisa Salsabela Bagus Dwi Saputra Bayastura, Shahnilna Fitrasha Bayu Surarso Bimastyaji Surya Ramadhan Budiyono Budiyono Calvin, Esagu John Catur Edi Widodo Chrisna Suhendi Cintika Oktavia Di Asih I Maruddani Di Mokhammad Hakim Ilmawan Dian Mariana L Manullang Dinar Mutiara Kusumo Nugraheni Dwi Ispriyanti Dyna Marisa Khairina eka rahmawati Ekky Rosita Singgih Wigati Endang Fatmawati Endang Fatmawati Fachry Abda El Rahman Faisal Fikri Utama Faliha Muthmainah Fath Ezzati Kavabilla Fatiya Nur Umma Ferry Hermawan Fiqria Devi Ariyani Firdonsyah, Arizona Gayuh Kresnawati Gertrude, Akello Ghifar Rahman Handayani, Sri Hanif Kusumasasmita Haritsa, Rifda Tsaqifarani Harjum Muharam Hasbi Yasin Hendri Setyawan Henny Widayanti, Henny Heriyanto Hizkia Christian Putra Setiadi Indra Jaya Infan Nur Kharismawan Intan Monica Hanmastiana Jafron Wasiq Hidayat Junta Zeniarja Kadarrisman, Vincensius Gunawan Slamet Kiswanto Kiswanto M. Afif Amirillah M. Andang Novianta Maharani, Chintya Ayu Mahrus Ali Maori, Nadia Annisa Maryono Maryono Maryono Maryono Masruroh, Fitriana Maulida Najwa, Maulida Mifta Ardianti Moch. Abdul Mukid Mochamad Arief Budihardjo Moh Ali Fikri mohamad jamil muhammad shodiq Muliyadi Muliyadi Munji Hanafi Mustafid Mustafid Mustaqim Mustaqim, Mustaqim Nisa Afida Izati Noor Azizah Nur Fitriyah Nurcahyanti, Tri Meida Nurul Hidayati Oktavia, Cintika Oky Dwi Nurhayati Pandu Anggara Paul, Gudoyi M Perdana, Ery Purwanto Purwanto Puspita Kartikasari Putri, Nitami Lestari R Rizal Isnanto R. Rizal Isnanto Rachmat Gernowo Rachmat Gernowo Rahmat Gernowo Rahmatul Akbar Ratna Kencana Putri Rini Nuraini Rita Rahmawati Rita Rahmawati Riva Amrulloh Riza Rizqi Robbi Arisandi Royani, Noorhanida Rukun Santoso Rully Rahadian Safitri, Adila Salma Farah Aliyah Sang Nur Cahya Widiutama Sari, Juwita Dwinda Silvia Elsa Suryana Siti Fadhilla Femadiyanti Sri Endah Moelya Artha Sri Sumiyati Sudarno Sudarno Sudarno Sudarno Sudarno utomo Sugito Sugito Sulardjaka Sulardjaka Suparti Suparti Syafrudin Syafrudin Tarno Tarno Tarno Tarno Tatik Widiharih Tatik Widiharih Ta’fif Lukman Afandi Tri Yani Elisabeth Nababan Ummayah, Putri Qodar Vincensius Gunawan Slamet Kadarrisman Wahyul Amien Syafei Whisnumurti Adhiwibowo Wibowo, Catur Edi Widiyatmoko, Carolus Borromeus Winahyu Handayani Yanuar Yoga Prasetyawan Yundari, Yundari